The integration of blockchain technology with the sixth generation (6G) networks offers a promising approach to enhance the reliability and trustworthiness of industrial Internet of Things (IIoT) systems. Since IIoT devices typically lack the capability to directly participate in blockchain consensus, drone networks offer a viable alternative by providing dynamic coverage and reducing dependence on fixed infrastructure such as centralized servers. Sharding is an effective method to improve the scalability of blockchain systems, yet existing sharding schemes overlook the complexity and dynamic nature of drone network topologies. These networks frequently experience changes due to drone mobility, task variations, and energy constraints, all of which can disrupt consensus communications. To address these challenges, we propose a time-varying graph-based blockchain sharding scheme (BSTVG) tailored for large-scale drone blockchain networks. The time-varying graph-based model captures the temporal dynamics of drone communications. We adopt an improved K-Means++ clustering algorithm that incorporates communication conditions to adapt network sharding. Additionally, we develop mechanisms for intra-shard consensus and cross-shard transaction processing. To accommodate node joins, exits, and significant topological changes, we introduce a slot–epoch coupling mechanism that dynamically adjusts the epoch length. We analyze the security of the proposed scheme and validate its performance through simulations. Experimental results demonstrate that our scheme not only enhances the throughput but also reduces energy consumption of the drone blockchain network.

Sliding network coding (SNC) has emerged as a promising solution for ultra-reliable and low-latency communication (URLLC) scenarios. The performance of SNC, particularly in terms of developing the encoding matrix and decoding strategy, is heavily influenced by the order h of the underlying Galois field,GF(2h). In this paper, we investigate high-order SNC, whereh> 1, to enhance transmission efficiency by employing a Vandermonde-based encoding matrix that ensures linear independence among coded packets. To maximize decoding efficiency, we design a structure-aware decoding strategy (SA-DS), which not only dynamically exploits the relationships between successfully decoded (SD) packets and the currently decoded (CD) packet, but also utilizes the first-packet deterministic decoding (FPDD) property of the Vandermonde matrix. Additionally, we develop a Markov chain-based performance analysis framework in terms of retransmission probability, packet error rate, and expected decoding delay. Numerical results demonstrate that in the evaluated settings, the proposed scheme outperforms several traditional schemes in the moderate-erasure region. In the low-erasure region, its advantage becomes particularly pronounced (reaching one to three orders of magnitude in both PER and retransmission probability while maintaining a comparable decoding delay), making it particularly suitable for URLLC applications.

The integration of blockchain technology with unmanned aerial vehicles (UAVs) offers considerable potential, enhancing cybersecurity and driving innovation within the UAV industry. However, due to the dynamic nature of UAVs and limited resources, existing blockchain consensus technologies cannot be directly applied to UAVs. To this end, we propose a chainbased optimized blockchain consensus protocol designed for UAV ad hoc networks, which employs the particle swarm optimization (PSO) algorithm to optimize chain consensus. We design several sub-protocols to cope with malicious nodes in the UAV network, node changes during UAV missions, topology changes. Numerical results show that our protocol increases throughput, reduces communication overhead, and enhances operation efficiency in UAV networks.

Owing to the advantage of flexible deployment of uncrewed aerial vehicles (UAVs), the problem of long-distance transmission of sensor devices in Internet of Things (IoT) networks can be effectively addressed. However, the line-of-sight (LoS) channels of UAVs also make the transmitted data vulnerable to interception by eavesdroppers. In this article, a security transmission scheme is proposed for UAV-aided IoT networks, in which a UAV with variable transmission power is deployed between the sensor device and the monitor. In addition, the concept of Age of Information (AoI) is introduced to measure the freshness of information, while the difference between the AoI of the eavesdropper and the monitor is considered as the metric for the security transmission performance. To enhance system security, we derive a closed-form solution for the AoI difference and propose a UAV deployment algorithm to maximize the gap. The simulation results demonstrate a strong agreement with the theoretical analysis, and the proposed method can significantly improve the security communication performance of the system compared with the traditional method.

Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.

In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.

In the Internet of Things (IoT) networks, monitoring information collection is critical for intelligent decision-making, which is a significant challenge for the sensors deployed at remote locations. Relay can effectively improve the transmission quality and transmission range of sensors by means of multi-hop transmission. It is an effective method for remote data collection in IoT networks. However, the lifetime of the relay may be dramatically reduced due to the heavy resource overhead for frequent short packet delivery. In this paper, we present an efficient relay transmission scheme for IoT networks, in which the frame aggregation technology is employed at the relay to reduce the resource overhead by sharing a common frame header and tail. Meanwhile, for the delay caused by frame aggregation, we analyze the freshness of the sensing data in terms of age of information (AoI) and take it as a constraint for the frame aggregation system. Besides, the optimal frame aggregation period is determined based on the closed-form expressions derived for the average AoI and transmission efficiency. Simulation results show that the theoretical analysis closely matches the simulations, and the proposed method significantly improves transmission efficiency compared to the traditional decode-and-forward method.

Wireless ad-hoc networks play a significant role in environments without fixed infrastructure, especially in military and emergency situations. Due to the openness and wide coverage of wireless channel, it is necessary to establish a secure transmission mechanism against potential eavesdroppers. Traditional secure transmission schemes almost rely on single path transmission and physical layer security techniques which may not provide enough secrecy when eavesdroppers are widely distributed. To address this, in this paper, we propose a multipath secure transmission mechanism for the sake of both guaranteeing the transmission security and maximizing the end-to-end throughput. Furthermore, we give the approximated closed-form expressions of multipath secrecy connection probability (SCP) relying on secret sharing, and a novel bilevel optimization problem is formulated with the constraints of the end-to-end delay and the derived SCP. Finally, simulation results show that our proposed mechanism is beneficial concerning secrecy performance in comparison to traditional mechanisms. Also, a near-optimal throughput is obtained.

Recently, the satellite network is emerged to guarantee the demand of seamless connectivity of Internet of Things (IoT) devices, which can provide services for IoT devices at anytime and anywhere. However, the satellite suffers from jamming attack due to its highly exposed satellite-ground links and spot-beams, which may cause severe security problems. In order to combat the jamming attack, we first analyze the performance of Satellite IoT (SIoT) in terms of the transmission rate. Then, we propose an anti-jamming strategy for SIoT by using the technique of beam switching, where a suitable satellite that offers sufficient spatial diversity can be chosen to swap the coverage with the attacked satellite. To this end, the coverage relationship between satellites and ground cells is investigated using the game theory and the satellite beam angle is further optimized to maximize the sum transmission rate of satellite clusters. Simulation results show that the proposed strategy can provide high achievable transmission rate for SIoT networks when jamming attacks happen.

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space–air–ground-integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in IoV, where aggregated packets can be relayed to a base station. In this article, we first propose an aggregators-aided random access scheme for IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a semi-Markov chain is used to analyze the average number of aggregated packets, and the metric of the average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

In the Internet of Things (IoT), numerous IoT devices are deployed for environment sensing, information collecting, and data transmitting to enable different operations, including patrol monitor, industrial automation, and system control. Considering the limited power and computation capability of IoT devices, mobile-edge computing (MEC) is applied to enhance the usage of IoT. Since unmanned aerial vehicles (UAVs) can be used as MEC servers, they become an efficient means to collect data packets and assist computation. In this article, we develop UAV-aided IoT systems, where the performance of data collection is analyzed in terms of packet loss rate and data quantity using a Markov chain. Then, in order to meet the diverse service requirements, the computation frequency of UAV is designed according to the preference coefficients of the cost on energy and time consumption. Finally, the system Age of Information (AoI) is considered to define the freshness of data packets, where the models of single-IoT device and multi-IoT devices with first-come–first-served (FCFS) principle and M/M/1 queuing are analyzed. The simulation results show that the proposed system is able to provide robust data collection and efficient computation for IoT devices.